Computational Approaches for Studying Transcription Elongation Control in Cancer

研究癌症转录延伸控制的计算方法

• 批准号: 10697319
• 负责人: Marta Iwanaszko
• 金额: $ 14.44万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-05 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10697319
• 关键词: Acute; Acute Lymphocytic Leukemia; Base Sequence; Bioinformatics; Cancer Biology; Chromatin Structure; Complement Factor B; Complex; Computational Biology; Computer Analysis; DNA Polymerase II; Data; Data Analyses; Defect; Development; Dimensions; Genes; Goals; MLL gene; Machine Learning; Malignant Neoplasms; Methods; Myelogenous; Oncogenic; RNA; Reproducibility; Research; Research Design; Research Personnel; Signal Transduction; Speed; Techniques; Therapeutic; Transcription Elongation; high standard; insight; large datasets; next generation; next generation sequencing; novel; skills; transcription factor; tumorigenesis

Project Summary The goal of this proposal is to provide bioinformatics support to advance research into the misregulation of transcription elongation in oncogenesis. For example, translocation of the mixed lineage leukemia (MLL) gene with a variety of genes expressing regulators of transcription elongation result in acute myeloid or acute lymphoid leukemia. Many of these factors are subunits of the Super Elongation Complex (SEC), which can enhance transcription elongation by two submodules: the positive regulator of transcription factor b (P-TEFb), which signals for the release of paused Pol II, and the RNA processivity factor ELL. A common denominator of these projects is the need for meticulous, biologically relevant computational analysis, which ultimately have several functions: decoding next generation sequencing data into a useful set of findings, combining data from multiple experimental platforms for a multi-dimensional perspective, and integrating machine learning approaches to extract data-specific signatures. These functions serve the goal of helping researchers, beginning with study design, data analysis, and further hypothesis building, to advance our understanding of cancer biology and to help evaluate cancer therapeutics. Dr. Iwanaszko will be responsible for implementing these approaches and providing expertise in both bioinformatics and computational biology to support cancer biologists in extracting more information from their data, speeding up the analysis, and to maintain a high standard of scientific rigor and reproducibility.

项目摘要 该提案的目标是提供生物信息学支持，以推进对基因表达调控失调的研究。 转录延长在肿瘤发生中的作用例如，混合谱系白血病（MLL）基因的易位 与表达转录延伸调节因子的多种基因一起导致急性骨髓或急性淋巴细胞 白血病这些因子中的许多是超延伸复合物（SEC）的亚基，其可以增强细胞内的细胞凋亡。 转录延伸由两个亚模块：转录因子B的正调节因子（P-TEF B）， 暂停Pol II释放的信号，以及RNA持续合成因子ELL。其中一个共同点是， 项目是需要细致的，生物相关的计算分析，最终有几个 功能：将下一代测序数据解码成一组有用的发现，将来自多个测序中心的数据 多维视角的实验平台，并整合机器学习方法， 提取数据特定的签名。这些功能服务于帮助研究人员的目标，从学习开始 设计，数据分析和进一步的假设建立，以促进我们对癌症生物学的理解， 帮助评估癌症治疗方法。Iwanaszko博士将负责实施这些方法， 提供生物信息学和计算生物学方面的专业知识，以支持癌症生物学家提取 从他们的数据中获得更多的信息，加快分析速度，并保持高标准的科学严谨性 和再现性。